



"""simple docstring"""







import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


a_							=    logging.get_logger(__name__)

a_							=    OrderedDict(
    [
        ("""audio-spectrogram-transformer""", """ASTFeatureExtractor"""),
        ("""beit""", """BeitFeatureExtractor"""),
        ("""chinese_clip""", """ChineseCLIPFeatureExtractor"""),
        ("""clap""", """ClapFeatureExtractor"""),
        ("""clip""", """CLIPFeatureExtractor"""),
        ("""clipseg""", """ViTFeatureExtractor"""),
        ("""conditional_detr""", """ConditionalDetrFeatureExtractor"""),
        ("""convnext""", """ConvNextFeatureExtractor"""),
        ("""cvt""", """ConvNextFeatureExtractor"""),
        ("""data2vec-audio""", """Wav2Vec2FeatureExtractor"""),
        ("""data2vec-vision""", """BeitFeatureExtractor"""),
        ("""deformable_detr""", """DeformableDetrFeatureExtractor"""),
        ("""deit""", """DeiTFeatureExtractor"""),
        ("""detr""", """DetrFeatureExtractor"""),
        ("""dinat""", """ViTFeatureExtractor"""),
        ("""donut-swin""", """DonutFeatureExtractor"""),
        ("""dpt""", """DPTFeatureExtractor"""),
        ("""encodec""", """EncodecFeatureExtractor"""),
        ("""flava""", """FlavaFeatureExtractor"""),
        ("""glpn""", """GLPNFeatureExtractor"""),
        ("""groupvit""", """CLIPFeatureExtractor"""),
        ("""hubert""", """Wav2Vec2FeatureExtractor"""),
        ("""imagegpt""", """ImageGPTFeatureExtractor"""),
        ("""layoutlmv2""", """LayoutLMv2FeatureExtractor"""),
        ("""layoutlmv3""", """LayoutLMv3FeatureExtractor"""),
        ("""levit""", """LevitFeatureExtractor"""),
        ("""maskformer""", """MaskFormerFeatureExtractor"""),
        ("""mctct""", """MCTCTFeatureExtractor"""),
        ("""mobilenet_v1""", """MobileNetV1FeatureExtractor"""),
        ("""mobilenet_v2""", """MobileNetV2FeatureExtractor"""),
        ("""mobilevit""", """MobileViTFeatureExtractor"""),
        ("""nat""", """ViTFeatureExtractor"""),
        ("""owlvit""", """OwlViTFeatureExtractor"""),
        ("""perceiver""", """PerceiverFeatureExtractor"""),
        ("""poolformer""", """PoolFormerFeatureExtractor"""),
        ("""regnet""", """ConvNextFeatureExtractor"""),
        ("""resnet""", """ConvNextFeatureExtractor"""),
        ("""segformer""", """SegformerFeatureExtractor"""),
        ("""sew""", """Wav2Vec2FeatureExtractor"""),
        ("""sew-d""", """Wav2Vec2FeatureExtractor"""),
        ("""speech_to_text""", """Speech2TextFeatureExtractor"""),
        ("""speecht5""", """SpeechT5FeatureExtractor"""),
        ("""swiftformer""", """ViTFeatureExtractor"""),
        ("""swin""", """ViTFeatureExtractor"""),
        ("""swinv2""", """ViTFeatureExtractor"""),
        ("""table-transformer""", """DetrFeatureExtractor"""),
        ("""timesformer""", """VideoMAEFeatureExtractor"""),
        ("""tvlt""", """TvltFeatureExtractor"""),
        ("""unispeech""", """Wav2Vec2FeatureExtractor"""),
        ("""unispeech-sat""", """Wav2Vec2FeatureExtractor"""),
        ("""van""", """ConvNextFeatureExtractor"""),
        ("""videomae""", """VideoMAEFeatureExtractor"""),
        ("""vilt""", """ViltFeatureExtractor"""),
        ("""vit""", """ViTFeatureExtractor"""),
        ("""vit_mae""", """ViTFeatureExtractor"""),
        ("""vit_msn""", """ViTFeatureExtractor"""),
        ("""wav2vec2""", """Wav2Vec2FeatureExtractor"""),
        ("""wav2vec2-conformer""", """Wav2Vec2FeatureExtractor"""),
        ("""wavlm""", """Wav2Vec2FeatureExtractor"""),
        ("""whisper""", """WhisperFeatureExtractor"""),
        ("""xclip""", """CLIPFeatureExtractor"""),
        ("""yolos""", """YolosFeatureExtractor"""),
    ]
)

a_							=    _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)

def     UpperCAmelCase_					(				__a   :			str						):




			'''simple docstring'''



			for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
						if class_name in extractors:
									_lowerCamelCase					:					int	       =    model_type_to_module_name(lowerCAmelCase__						)

									_lowerCamelCase					:					Optional[int]	       =    importlib.import_module(f".{module_name}"      ,							'transformers.models'						)
									try:
												return getattr(lowerCAmelCase__      ,							lowerCAmelCase__						)
									except AttributeError:
												continue

			for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
						if getattr(lowerCAmelCase__      ,							'__name__'      ,							lowerCAmelCase__						) == class_name:
									return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
			_lowerCamelCase					:					List[str]	       =    importlib.import_module('transformers'						)
			if hasattr(lowerCAmelCase__      ,							lowerCAmelCase__						):
						return getattr(lowerCAmelCase__      ,							lowerCAmelCase__						)

			return None

def     UpperCAmelCase_					(				__a   :			Union[str, os.PathLike]      ,							__a   :			Optional[Union[str, os.PathLike]] = None      ,							__a   :			bool = False      ,							__a   :			bool = False      ,							__a   :			Optional[Dict[str, str]] = None      ,							__a   :			Optional[Union[bool, str]] = None      ,							__a   :			Optional[str] = None      ,							__a   :			bool = False      ,							**__a   :			Optional[int]      ,							):




			'''simple docstring'''



			_lowerCamelCase					:					List[str]	       =    get_file_from_repo(
			    lowerCAmelCase__      ,							lowerCAmelCase__      ,							cache_dir=lowerCAmelCase__      ,							force_download=lowerCAmelCase__      ,							resume_download=lowerCAmelCase__      ,							proxies=lowerCAmelCase__      ,							use_auth_token=lowerCAmelCase__      ,							revision=lowerCAmelCase__      ,							local_files_only=lowerCAmelCase__      ,							)
			if resolved_config_file is None:
						logger.info(
						    'Could not locate the feature extractor configuration file, will try to use the model config instead.'						)
						return {}

			with open(lowerCAmelCase__      ,							encoding='utf-8'						) as reader:
						return json.load(lowerCAmelCase__						)







class 					A_:







			"""simple docstring"""



			def __init__(	self							):
						raise EnvironmentError(
						    'AutoFeatureExtractor is designed to be instantiated '
						    'using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method.'							)
			@classmethod
			@replace_list_option_in_docstrings(SCREAMING_SNAKE_CASE_							)
			def    _lowerCAmelCase		(	cls				,   A				,   **A							):
						_lowerCamelCase					:					Tuple	       =    kwargs.pop('config'				,   SCREAMING_SNAKE_CASE_							)
						_lowerCamelCase					:					str	       =    kwargs.pop('trust_remote_code'				,   SCREAMING_SNAKE_CASE_							)
						_lowerCamelCase					:					str	       =    True

						_lowerCamelCase					:					Optional[int]	       =    FeatureExtractionMixin.get_feature_extractor_dict(SCREAMING_SNAKE_CASE_				,   **SCREAMING_SNAKE_CASE_							)
						_lowerCamelCase					:					Optional[int]	       =    config_dict.get('feature_extractor_type'				,   SCREAMING_SNAKE_CASE_							)
						_lowerCamelCase					:					Any	       =    None
						if "AutoFeatureExtractor" in config_dict.get('auto_map'				,   {}							):
									_lowerCamelCase					:					List[Any]	       =    config_dict["""auto_map"""]["""AutoFeatureExtractor"""]

						# If we don't find the feature extractor class in the feature extractor config, let's try the model config.
						if feature_extractor_class is None and feature_extractor_auto_map is None:
									if not isinstance(SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_							):
												_lowerCamelCase					:					Union[str, Any]	       =    AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_				,   **SCREAMING_SNAKE_CASE_							)
									# It could be in `config.feature_extractor_type``
									_lowerCamelCase					:					Tuple	       =    getattr(SCREAMING_SNAKE_CASE_				,   'feature_extractor_type'				,   SCREAMING_SNAKE_CASE_							)
									if hasattr(SCREAMING_SNAKE_CASE_				,   'auto_map'							) and "AutoFeatureExtractor" in config.auto_map:
												_lowerCamelCase					:					Union[str, Any]	       =    config.auto_map["""AutoFeatureExtractor"""]

						if feature_extractor_class is not None:
									_lowerCamelCase					:					Optional[int]	       =    feature_extractor_class_from_name(SCREAMING_SNAKE_CASE_							)

						_lowerCamelCase					:					int	       =    feature_extractor_auto_map is not None
						_lowerCamelCase					:					Tuple	       =    feature_extractor_class is not None or type(SCREAMING_SNAKE_CASE_							) in FEATURE_EXTRACTOR_MAPPING
						_lowerCamelCase					:					Union[str, Any]	       =    resolve_trust_remote_code(
						    SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_							)

						if has_remote_code and trust_remote_code:
									_lowerCamelCase					:					List[Any]	       =    get_class_from_dynamic_module(
									    SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_				,   **SCREAMING_SNAKE_CASE_							)
									_lowerCamelCase					:					List[Any]	       =    kwargs.pop('code_revision'				,   SCREAMING_SNAKE_CASE_							)
									if os.path.isdir(SCREAMING_SNAKE_CASE_							):
												feature_extractor_class.register_for_auto_class()
									return feature_extractor_class.from_dict(SCREAMING_SNAKE_CASE_				,   **SCREAMING_SNAKE_CASE_							)
						elif feature_extractor_class is not None:
									return feature_extractor_class.from_dict(SCREAMING_SNAKE_CASE_				,   **SCREAMING_SNAKE_CASE_							)
						# Last try: we use the FEATURE_EXTRACTOR_MAPPING.
						elif type(SCREAMING_SNAKE_CASE_							) in FEATURE_EXTRACTOR_MAPPING:
									_lowerCamelCase					:					Tuple	       =    FEATURE_EXTRACTOR_MAPPING[type(SCREAMING_SNAKE_CASE_							)]
									return feature_extractor_class.from_dict(SCREAMING_SNAKE_CASE_				,   **SCREAMING_SNAKE_CASE_							)

						raise ValueError(
						    F"Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a "
						    F"`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following "
						    F"`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys()							)}"							)

			@staticmethod
			def    _lowerCAmelCase		(	A				,   A							):
						FEATURE_EXTRACTOR_MAPPING.register(SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_							)










from typing import TYPE_CHECKING

from ...utils import _LazyModule


a      :  int			     =			{'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}


if TYPE_CHECKING:
	from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
	import sys

	a      :  int			     =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






